一句话：提出基于学习的自动许可证预测方法 ALP，为代码变更预测合适开源许可证，在 700 个项目上 micro F1 达 92.5%。
开源许可证规范软件的重分发、复用与修改，软件变更过程中确保许可证兼容并避免限制冲突，是保护其商业使用的关键。然而，为变更选择合适许可证需要经验与大量人工，需检查、理解和比较多种许可证及其与变更的关系，且缺乏最先进方法。为此，论文提出自动许可证预测（ALP），一种基于学习的方法与工具，用于在软件变更时预测许可证。作者在 700 个开源项目上进行广泛评估，结果表明 ALP 不仅总体预测准确率高（micro F1 为 92.5%），而且在所有许可证类型上均取得高准确率。
Open source software licenses regulate the circumstances under which software can be redistributed, reused and modified. Ensuring license compatibility and preventing license restriction conflicts among source code during software changes are the key to protect their commercial use. However, selecting the appropriate licenses for software changes requires lots of experience and manual effort that involve examining, assimilating and comparing various licenses as well as understanding their relationships with software changes. Worse still, there is no state-of-the-art methodology to provide this capability. Motivated by this observation, we propose in this paper Automatic License Prediction (ALP), a novel learning-based method and tool for predicting licenses as software changes. An extensive evaluation of ALP on predicting licenses in 700 open source projects demonstrate its effectiveness: ALP can achieve not only a high overall prediction accuracy (92.5% in micro F1 score) but also high accuracies across all license types.
DOI 原文 ·
@inproceedings{paperbot5326,
title = {Predicting Licenses for Changed Source Code},
author = {Xiaoyu Liu and LiGuo Huang and Jidong Ge and Vincent Ng},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00070}
}